New Data Science Section launches

Earlier this year, the Data Science Section (DSS) was formed to serve the emerging needs of the data science profession. The committee, comprising representatives from business, industry, academia and government, identified a number of areas where the RSS could support the data science community, including facilitation of professional and ethical frameworks and encouraging sound statistical methods.

The first work stream to be undertaken aims to support the industrialisation and professionalisation of data science. The DSS propose 12 broad questions to be addressed:

  1. What does great data science look like?
  2. What does a good data science workflow look like?
  3. What kind of problems can be addressed by data science?
  4. What are t he characteristics of the ideal data scientist?
  5. How should an organisation start a data science function?
  6. How should data science fit into the structure of an organisation?
  7. How should business practices change to make a success of data science?
  8. What do executives and managers need to learn about data science?
  9. How can an organisation build a coherent data science capability from a collection of data science projects?
  10. What career paths are available to a data scientist?
  11. How can data scientists measure the value they create?
  12. What is a data scientist’s responsibility to wider society?

The committee asked the data science community whether these were t he right questions at a collaborative meeting held at the RSS on June 19.

Industrialisation and professionalisation of data science - launch event

The event was opened and closed by DSS chair Andy Garrett, who presented the DSS remit. Harry Powell and Fran Bennett (DSS committee) gave an overview of the 12 questions, and Martin Goodson (DSS committee) used examples of data science workflows gone wrong to illustrate how the DSS might help. There were invited responses from two senior data scientists, Sofia Olhede - UCL Big Data Institute and Juan Fernandez - Kantar Milward Brown, and RSS president Sir David Spiegelhalter, before a lively Q&A session with the audience where a broad range of views were expressed. The meeting was followed by a successful networking reception sponsored by Evolution AI.

Some key points from t he discussion were:

  • Sofia empathised with the difficulty in achieving a high level of expertise in all the diverse skills required for good data science. She highlighted two key data science skills: the ability to collaborate across disciplines, and harnessing ubiquitous sensing data (often pertaining to human subjects, not collected for specific purpose).
  • Juan identified strongly with the 12 questions. He spoke of his own challenges in defining and getting traction for data science at a strategic level, and developing collaborative relationships between data science and existing technology teams to deliver scalable, secure, stable products. He added that managers and businesses needed to let data scientists concentrate on what they are good at.
  • Sir David felt that given statistics is a core part of data science, the RSS can play an important role in helping data science to mature as a discipline.
  • The audience generally agreed that professionalisation of data science is desirable but raised several questions about what that would look like.

The full event is available to watch on YouTube.

Next steps and how you can get involved
The launch event was an opportunity to gather initial feedback from the community on what role the DSS can play in supporting data science as a profession. The 12 questions which will define future work areas are available for comment on Github.

The next phase will be forming collaborative working groups to consider f eedback and agree actions to start addressing the issues identified. If you have an interest in any of the 12 areas please get in touch with the section through one of the following channels:

Twitter: https://twitter.com/RSS_DSS
Slack: https://rssdatascience.slack.com
LinkedIN: https://www.linkedin.com/company-beta/11150048/
Github: https://github.com/rssdatascience

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